Folio Search · free preview

Papers on “autonomous vehicles self-driving safety”

Live results from Semantic Scholar, CrossRef and OpenAlex — no account needed to look.

1. A Survey of Autonomous Driving: Common Practices and Emerging Technologies

   Ekim Yurtsever, Jacob Lambert, Alexander Carballo, et al. · 2020 · IEEE Access · 1,801 cites

   Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art is improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions including localization, mapping, perception, planning, and human machine interfaces, were thoroughly reviewed. Furthermore, many state-of-the-art algorithms were implemented and compared on our own platform in a re

2. A survey of deep learning techniques for autonomous driving

   Sorin Grigorescu, Bogdan Trăsnea, Tiberiu Cocias, et al. · 2019 · Journal of Field Robotics · 1,790 cites

   Abstract The last decade witnessed increasingly rapid progress in self‐driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence (AI). The objective of this paper is to survey the current state‐of‐the‐art on deep learning technologies used in autonomous driving. We start by presenting AI‐based self‐driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration, and motion control algorithms. We investigate both the modular perception‐planning‐action pipeline, where each

3. Planning and Decision-Making for Autonomous Vehicles

   Wilko Schwarting, Javier Alonso–Mora, Daniela Rus · 2018 · Annual Review of Control Robotics and Autonomous Systems · 969 cites

   In this review, we provide an overview of emerging trends and challenges in the field of intelligent and autonomous, or self-driving, vehicles. Recent advances in the field of perception, planning, and decision-making for autonomous vehicles have led to great improvements in functional capabilities, with several prototypes already driving on our roads and streets. Yet challenges remain regarding guaranteed performance and safety under all driving circumstances. For instance, planning methods that provide safe and system-compliant performance in complex, cluttered environments while modeling the uncertain interaction with other traffic participants are required. Furthermore, new paradigms, su

4. Autonomous Vehicle Implementation Predictions: Implications for Transport Planning

   Todd Litman · 2015 · Transportation Research Board 94th Annual MeetingTransportation Research Board · 864 cites

   This paper explores the impacts that autonomous (also called self-driving, driverless or robotic) vehicles are likely to have on travel demands and transportation planning. It discusses autonomous vehicle benefits and costs, predicts their likely development and implementation based on experience with previous vehicle technologies, and explores how they will affect planning decisions such as optimal road, parking and public transit supply. The analysis indicates that some benefits, such as independent mobility for affluent non-drivers, may begin in the 2020s or 2030s, but most impacts, including reduced traffic and parking congestion (and therefore road and parking facility supply requiremen

5. Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

   De Jong Yeong, Gustavo Velasco-Hernandez, John M. Barry, et al. · 2021 · Sensors · 859 cites

   With the significant advancement of sensor and communication technology and the reliable application of obstacle detection techniques and algorithms, automated driving is becoming a pivotal technology that can revolutionize the future of transportation and mobility. Sensors are fundamental to the perception of vehicle surroundings in an automated driving system, and the use and performance of multiple integrated sensors can directly determine the safety and feasibility of automated driving vehicles. Sensor calibration is the foundation block of any autonomous system and its constituent sensors and must be performed correctly before sensor fusion and obstacle detection processes may be implem

6. Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions

   Christos Katrakazas, Mohammed Quddus, Wen‐Hua Chen, et al. · 2015 · Transportation Research Part C Emerging Technologies · 804 cites

   Currently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion, lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have b

7. Perceptions of autonomous vehicles: Relationships with road users, risk, gender and age

   Lynn Hulse, Hui Xie, Edwin R. Galea · 2017 · Safety Science · 699 cites

8. Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems

   On-Road Automated Driving (ORAD) Committee · 2014 · 686 cites

   This Information Report provides a taxonomy for motor vehicle automation ranging in level from no automation to full automation. However, it provides detailed definitions only for the highest three levels of automation provided in the taxonomy (namely, conditional, high and full automation) in the context of motor vehicles (hereafter also referred to as “vehicle” or “vehicles”) and their operation on public roadways. These latter levels of advanced automation refer to cases in which the dynamic driving task is performed entirely by an automated driving system during a given driving mode or trip. Popular, media, and legislative references to “autonomous” or “self-driving” vehicles encompass s